Introduction
With the emergence of the global greenhouse effect and extreme climate, the issue of “carbon emission” has attracted worldwide attention. In 1992, more than 150 countries in the world jointly signed the United Nations Framework Convention on Climate Change (UNFCCC), which put forward a framework international convention to address global warming with the control of carbon dioxide emissions for the first time. China not only has a large population but is also the world’s factory, consuming a large amount of fossil energy every year. Limiting carbon emissions will have a negative impact on China’s traditional economic development model, but to maintain the world’s ecological environment, China has taken the initiative to reduce carbon emissions. In 2021, the State Council of China issued the Action Plan for Peaking Carbon Emissions Before 2030, which has achieved a steady decline in national carbon emissions each year.
Reducing agricultural carbon emissions is also an important part of China’s efforts to achieve carbon peaking and carbon neutrality. However, agricultural development faces the dual dilemma of green low-carbon transformation and high-quality development of the agricultural economy. In 2022, the per capita disposable income of China’s rural residents was only 20,133 yuan, and China’s agricultural economy is characterized by a large population and low per capita income. China’s agriculture plays two important roles. One role is to produce agricultural products to ensure that farmers have food to eat, are not hungry, and maintain the basic life of farmers. The development of agriculture is also conducive to maintaining national and social stability. On the other hand, agriculture is the main source of income for farmers, especially for the majority of farmers who are poorly educated and have few job opportunities. Agricultural income is their lifeline and the main source of income for them to send their children to school and support elderly people. Agricultural development concerns the livelihoods of the vast majority of Chinese farmers, and limiting agricultural carbon emissions is also the most difficult task. Therefore, this work has important practical significance for China’s agricultural economic development, ecological protection and policy formulation. At the same time, this study provides a reference for the United Nations carbon emission policy to consider the actual difficulties of Chinese farmers.
The impact of carbon emissions on agricultural development is a hot topic worldwide, and relevant research results have focused mainly on the following three aspects.
First, the existing research has focused on agroecological efficiency. The concept of ecological efficiency was first proposed by German scholars Schaltegger and Sturm in 1990 [1]. In 2003, the concept of “agricultural sustainable development” was incorporated into the scope of agricultural policy formulation. Agenda 2000 requires all EU Member States to comply with minimum environmental standards and promote ecological efficiency and ecological equity [2]. Life cycle assessment and data envelopment analysis are important methods for evaluating eco-efficiency [3]. In the evaluation of eco-efficiency performance at the agricultural sector level in EU countries, it was found that 10 countries had good ecological effects and 18 countries had poor ecological effects [4]. Greenhouse gas emissions from agricultural output, the labor force, agricultural acreage, and nitrogen, phosphorus, and potassium fertilizers increased significantly in 24 selected EU member states [5]. Agriculture is the second largest source of greenhouse gas emissions, with agricultural soils accounting for a major share [6]. There is a conflict between industrial agriculture and sustainable agriculture, and small farms are difficult to choose [7]. The Dutch agricultural sector has the highest ammonia emissions per hectare of farmland, nitrogen and phosphorus surpluses, and pesticide use in the EU [8]. Polish farms, under pressure from greenhouse gas emissions, were able to reduce conventional inputs by almost a quarter without reducing production [9]. China’s agricultural ecological efficiency has steadily improved. With land, machinery, labor, fertilizer, pesticides, and agricultural film as input variables and economic output and agricultural carbon emissions as output variables, the ultraefficient SBM model is adopted, and the results show that agricultural ecological efficiency in China has a stable and fluctuating trend of growth [10]. Technological progress, agricultural infrastructure and human capital improvement contributed positively to the growth of agricultural production efficiency [11]. Economic complexity affects the ecological footprint and leads to environmental degradation [12].
Second, the existing research has focused on the measurement and method of agricultural carbon emissions. Agricultural carbon emissions are affected by many factors. Scholars from various countries have attempted to measure carbon emissions from multiple perspectives, but their conclusions are inconsistent in terms of sample selection, estimation methods and calculation results. Carbon cycle analysis can measure carbon emissions from energy use and primary fuels, electricity, fertilizers, lime, pesticides, irrigation, seed production and agricultural machinery [13]. Land use, input and soil management, good agricultural management and organic farming practices contribute to reducing carbon emissions [14]. Soil microbes are a critical ecological parameter in the ecosystem’s C cycle [15]. The crop production index and livestock production index have different impacts on carbon emissions [16]. The propensity score matching method revealed that agricultural production has an effect on reducing agricultural carbon emissions [17]. Energy carbon emission efficiency in rural China is on the rise, and technological change can reduce carbon emissions [18]. The income structure of national and rural residents changes China’s agricultural CO2 emissions [19]. The exponential method, nuclear density analysis and convergence analysis can be used to quantitatively analyze the influence of the animal husbandry structure on carbon emissions [20]. Carbon emissions in the Asia-Pacific region and China have also become popular research topics. The SBM-bad model of the factor analysis system was used to calculate the agroecological output of each province separately [21]. Carbon emission rights can improve the total factor productivity of agricultural enterprises and promote green innovation [22]. An increase in agricultural land area in Nepal triggered increased CO2 emissions [23]. Both agricultural carbon emissions and carbon emission intensity showed a downward trend in Zhejiang Province [24]. According to MK trend analysis, Monte Carlo simulation, and other methods, China’s provincial carbon peak-shifting echelons can be divided into five carbon-peak-shifting echelons: basic realization, early realization, early realization, on-time realization, and possibly delayed realization [25]. Economic growth and renewable energy can predict CO2 emission levels in the MINT economies [26]. Green technology innovations and renewable energy help limit carbon emissions [27].
Third, other studies have focused on the impact of agricultural carbon emissions on the economy. Academics have also found a strong link between agricultural carbon emissions and economic growth. Through the study of the environmental Kuznets curve (EKC) hypothesis in Brazil, Russia, India, China, and South Africa, it has been found that agricultural activities and energy use affect the environment [28]. In the European Union, the relationship between economic performance and CO2 emissions has attracted the interest of the scientific community and has become a priority in the Common Agricultural Policy, where there is a cointegration relationship between agricultural carbon equivalent and per capita income in the agricultural sector of EU countries [29]. There is a cointegration relationship between economic growth and carbon emission intensity caused by chemical fertilizer, pesticides, agricultural film, agricultural diesel, and the five carbon sources of agriculture [30]. An increase in agricultural carbon emission intensity in China will reduce the level of agricultural trade and the overall development level of the agricultural economy [31]. Agricultural industrial upgrading and rural electricity consumption are significantly positively correlated with agricultural carbon emissions [32]. Economic growth, energy use, agricultural productivity, and forest area in Kazakhstan have dynamic effects on carbon dioxide (CO2) emissions [33]. There is heterogeneous dynamic causality among the intensity of energy use, land agglomeration, and carbon dioxide emissions [34]. An increase in forest area leads to a decrease in carbon emissions [35]. In Peru, economic growth, renewable energy use, and agricultural land expansion have an impact on CO2 emissions, and increasing renewable energy use can reduce CO2 emissions [36]. There is a relationship between agricultural production, economic growth, and CO2 emissions in Pakistan, which decreases as barley and sorghum production increases [37]. The development of the regional digital economy has reduced agricultural carbon emissions significantly [38]. Encouraging agricultural science and technology research and innovation can promote the green and low-carbon development of agriculture [39]. The implementation of policies for high-standard farmland construction has a continuous inhibitory effect on agricultural carbon emissions [40]. The contribution rate of agricultural science and technology progress and total agricultural output value have an impact on carbon emissions [41]. Agricultural mechanization can reduce carbon emissions of the major grain production areas [42]. Both agricultural carbon emissions and agricultural carbon intensity conform to the inverted U-shape assumed by the EKC [43]. Provincial demonstration policies for clean energy have an impact on carbon emissions [44]. A reduction in carbon emission intensity will promote high-quality economic development [45]. Agricultural economic growth and net agricultural carbon emissions can also be decoupled [46]. There is a decoupling state between agricultural carbon emissions and agricultural economic growth in the Hotan area [47]. There is a bidirectional causality between agricultural productivity and renewable energy consumption [48]. The impact of GDP growth escalates the ratio of CO2. China’s agricultural carbon emissions show an inverted U-shaped trend, but the overall growth rate shows a gradual declining trend [49].
In summary, existing studies have elaborated on the concept, measurement methods, influence, existing problems, and possible solutions of eco-efficiency related to agricultural carbon emissions, which provides a basis for subsequent studies of the impact of carbon emissions on the agricultural economy. However, there is a lack of systematic discussion about agricultural carbon emissions and China’s agricultural economic growth. In the critical period of China’s agricultural upgrading, it is of great theoretical and practical significance to discuss the impact of agricultural carbon emissions on agricultural economic development. This paper uses the environmental Kuznets curve theory to carry out an empirical study on the effect of agricultural carbon emissions on China’s agricultural economic development.
Materials and Methods
The EKC Theory and Research Hypotheses
For the first time, American economists Grossman and Krueger conducted an empirical study on the relationship between environmental quality and per capita income and noted that pollution increases with increasing per capita GDP at low income levels but decreases with increasing GDP at high income levels. Panayotou first elaborated on the environmental Kuznets curve (EKC) in 1993, revealing the inverse U-shaped relationship between environmental quality and income. China’s agricultural development is still in the initial stage, agricultural production and planting depend on farmers, and the agricultural production mode is relatively rough. Carbon emissions are gaseous substances produced in agricultural production, and carbon dioxide will cause greenhouse effects, which will have adverse effects on the natural environment and social and economic activities. Modern agricultural production needs to consume electrical energy, fertilizer, agricultural plastic film, and arable land resources, and agricultural machinery and equipment need to be invested in these materials. According to the environmental Kuznets function theory and literature review, in the initial stage of agricultural development, the amount of input and consumption of materials is proportional to the agricultural carbon emissions generated, and the inputs of electrical energy, fertilizer, agricultural plastic film, cultivated land resources and agricultural machinery and equipment also affect agricultural economic output. Controlling carbon emissions may indirectly affect agricultural economic output. Therefore, combining the environmental Kuznets curve and the characteristics of the current stage of Chinese agriculture, the following hypothesis is given: H1: China’s agricultural carbon emissions positively affect its agricultural economic growth.
Based on the above research, an index system of agricultural carbon emissions affecting agricultural economic development was selected and constructed. Why not calculate agricultural emissions on this basis, and use this carbon calculation to extrapolate the causal relationship with the agricultural economy? This is mainly based on this consideration. First, although there are rough calculation standards for carbon emissions of each influencing factor at home and abroad, the data are not uniform, and the calculated carbon emissions results will be controversial and affect the causal judgment of agricultural economic growth. Second, this paper mainly focuses on determining the positive or negative effects of carbon emission factors on agricultural economic growth and does not require a clear calculation of its impact coefficient. Therefore, the selection of influencing factors of carbon emissions as factors affecting agricultural economic growth can satisfy the inference conditions and can be set as intermediate variables without calculating specific data on agricultural carbon emissions.
Data and Sample Information
This paper takes the agricultural carbon emission index and agricultural economic output of 31 provinces in China from 2019–2022 as examples to analyze the impact of the agricultural carbon emission index on China’s agricultural economic development through panel data. According to the above summary and theoretical research, this paper selected 10 indicators, namely, the Output of Agriculture Economic (OAE), Cultivated Land (CA), Total Power of Agricultural Machinery (TPAM), Rural Electricity Consumption (REC), Amount of Agricultural Chemical Fertilizer Applied (AACF), Amount of Plastic Film used in Agriculture (APF), Number of Biogas Projects (NBP), Reservoir Capacity (RC), Waterlogging Control Area (WCA), and Soil Erosion Control Area (SECA), in addition to the year and province. A total of 1488 samples were selected. The data were selected from the 2020-2023 edition of the China Rural Statistical Yearbook published by the Rural Socioeconomic Survey Department of the National Bureau of Statistics of China. The data are collected by the National Bureau of Statistics price survey, which collects and sorts the basic statistical information of rural social and economic development and is more comprehensive, objective, and accurate. However, it is unbalanced panel data due to the presence of missing values. For missing data in sample data, this paper adopts the interpolation method.
Table 1.
Indicator description
Among the 10 variables selected in addition to the year and province, this paper takes the output of the agricultural economy (OAE) as the dependent variable, and the total output values of agriculture, forestry, animal husbandry, and fishery (OAFAF) are chosen as representative variables.
Independent variables are the main influencing factors of carbon emissions in China’s agricultural economy, including cultivated land (CA), total power of agricultural machinery (TPAM), rural electricity consumption (REC), amount of agricultural chemical fertilizer applied (AACF), and amount of plastic film used in agriculture (APF). The control variables are the number of biogas projects (NBP), the reservoir capacity (RC), the waterlogging control area (WCA), and the soil erosion control area (SECA). The specific indicators are defined below (Table 1).
In Table 1, for the statistical analysis of the sample data, the sample data are from 2019-2022. For the provincial data, the sequence number is 1-31. The maximum and minimum values, average values and standard deviations of the Output of Agriculture Economic (OAE), the Cultivated land (CA), the Total Power of Agricultural Machinery (TPAM), Rural Electricity Consumption (REC), the Amount of Agricultural Chemical Fertilizer applied (AACF), the Amount of Plastic Film used in agriculture (APF), the Number of Biogas Projects (NBP), the Reservoir Capacity (RC), the Waterlogging Control Area (WCA), and Soil Erosion Control Area (SECA) are as follows (Table 2):
In Table 2, the Output of Agriculture Economic (OAE) is taken as an example. The province with the largest Output of Agriculture Economic (OAE) was 121.307 billion yuan, while the province with the smallest Output (OAE) was 21.28 billion yuan. Other indicators are not enumerated. China’s agricultural economy and agricultural carbon emissions are very different among provinces, mainly due to the uneven distribution of agricultural resources and the uneven development of the agricultural economy in China. China has municipalities directly under the control of the central government, and the level of these municipalities is the same as the administrative level of provinces, but their economic activities are mainly urban economic activities, basically less basic farmland and cultivated land, farmers have been transformed into citizens, agricultural production activities are less common, and statistics are often conducted to collect data on the nonagricultural economy, so the statistical indicators of agricultural economic components are low.
According to the statistical analysis of the above sample data, the index of each indicator has a large gap, and the units are inconsistent. To further study the correlation and causality between the indicators of the sample data, the data need to be processed in the early stage. Therefore, the logarithm of economic data is processed in this paper to eliminate the impact of the dimensions between the original data and variables, as well as the changes between the values. The basic formula of the model is Formula 1:
In Formula 1,
In Table 3, taking the Output of Agriculture Economic (OAE) as an example, the province with the largest Output of Agriculture Economic (OAE) in X1 is 121.307 billion yuan, and the logarithm is 7.101. The province with the smallest value (OAE) is 21.28 billion yuan, and the logarithm is 3.058. The gap between the original data is relatively large, but after logarithms are taken, they are all close. To ensure that the value is positive after taking the natural logarithm, a value greater than 1 is given for the original data below 1.
Table 2.
Descriptive Statistics
Research Methods
Pooled Regression Model
To study the relationships among cultivated land (CA), the total power of agricultural machinery (TPAM), rural electricity consumption (REC), the amount of agricultural chemical fertilizer applied (AACF), the amount of plastic film used in agriculture (APF) and the total output of the agricultural economy (OAE) in each region, the location and time variables were first ignored, and the whole sample data were regarded as a cross-sectional database for pooled regression. The OLS multiple regression function is as follows:
Table 3.
Descriptive Statistics
Table 4.
The description of the MLS regression results
[i] t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1‘*’indicates that there is at least a 90% chance that the event will occur. ‘**’indicates that there is at least a 95% chance that the eventwill occur, and ‘***’indicates that there is at least a 99% chance that the event will occur. The same as below.
In Formula 2,
According to existing studies, the emission coefficients of agricultural carbon emission sources are as follows: fertilizer, 0.896 kg/kg (West and Marland, 2002); pesticide, 4.934 kg/kg; agricultural film, 5.180 kg/ kg; agricultural irrigation, 20.476 kg/ha; and agricultural farming, 312.600 kg/ha [1]. Therefore, the annual carbon emissions of Hunan’s agriculture can be preliminarily calculated. These emission coefficients are all positive, and the total carbon emissions are directly proportional to the above influencing factors. Carbon emissions increase with the growth of various indicators. This paper mainly focuses on the impact of carbon emission factors on agricultural economic development and conducts multiple linear regression on the above factors. Through multivariate least squares regression (mixed estimation), the results of multiple regression are as follows (Table 4).
Table 4 shows that in Model y (1), the causal relationships between the relevant influencing factors of agricultural carbon emissions (cultivated land (CA), total power of agricultural machinery (TPAM), rural electricity consumption (REC), amount of agricultural chemical fertilizer applied (AACF), and amount of plastic film used in agriculture (APF)) are significant. Among them, TPAM, REC, AACF and APF had positive impacts, and the total power of agricultural machinery (TPAM) had a greater positive impact, with an impact coefficient of 0.417, followed by the amount of agricultural chemical fertilizer applied (AACF), with an impact coefficient of 0.332. However, cultivated land (CA) has a negative impact on the Output of Agriculture Economic (OAE), and the complex impact coefficient is mainly because China has a large population and little land, and agricultural production does not increase as cultivated land increases. In contrast, due to the expansion of urbanization, cultivated land is shrinking, but the total agricultural economy is constantly growing.
In y (2), y (3), y (4) and y (5), cultivated land (CA) has no significant impact on the Output of Agriculture Economic (OAE), but in addition, the Total Power of Agricultural Machinery (TPAM), Rural Electricity Consumption (REC), the Amount of Agricultural Chemical Fertilizer Applied (AACF), and the Amount of Plastic Film used in Agriculture (APF) still have a positive impact on the Output of Agriculture Economic (OAE). Cultivated land (CA), the total power of agricultural machinery (TPAM), the amount of agricultural chemical fertilizer applied (AACF), and the amount of plastic film used in agriculture (APF) have significant effects on carbon emissions and agricultural economic development. The original Hypotheses H1 are accepted.
According to the endogeneity test, after the mixed regression of the sample data, the F value is 4.51, and the P value is 0.005. Through the endogeneity test, it can be shown that the key variables of the model are not correlated with the error term.
Fixed Effect Model (FE) and Random Effects Model (RE)
To further analyze the influence of carbon emission factors such as cultivated land (CA), total power of agricultural machinery (TPAM), rural electricity consumption (REC), amount of agricultural chemical fertilizer applied (AACF), and amount of plastic film used in agriculture (APF) on the output of agricultural economy (OAE), fixed effects and random effects were analyzed.
Table 5.
Descriptions of the FE regression results
Fixed effects take into account both individual traits and essential error distinctions, but their individual traits are “fixed”. In general, the model formula (sample formula) of the fixed effect is shown in Formula 3 below.
In Formula 3,
Given individual
In Formula 4, the variable has the same meaning as in Formula 3. In addition,
The fixed effect simulation results are as follows (Table 5).
According to the fixed effect regression results, the total power of agricultural machinery (TPAM), rural electricity consumption (REC), and amount of agricultural chemical fertilizer applied (AACF) had positive impacts on the output of agricultural economy (OAE), and the total power of agricultural machinery (TPAM) had the largest influence coefficient (0.566), while the cultivated land (CA) and amount of plastic film used in agriculture (APF) had no significant impact on the output of agricultural economy (OAE).
Random effects also take into account individual traits and essential error distinctions, but their individual traits are “random.” In general, the model formula (sample formula) for random effects is shown as follows (Formula 6):
In Formula 6, the variable has the same meaning as in Formula 3, but individual effects
When
The random effect results of the model are as follows (Table 6).
In Table 6, through the above random effects, it can be seen that the total power of agricultural machinery (TPAM), rural electricity consumption (REC) and the amount of plastic film used in agriculture (APF) have positive impacts on the output of agricultural economy (OAE), and the total power of agricultural machinery (TPAM) influence coefficient is the largest, at 0.566. Cultivated land (CA) and the amount of agricultural chemical fertilizer applied (AACF) had no significant impact on the output of agricultural economy (OAE).
According to the Hausman test, the P value is close to 0, and both the fixed and random effects of this model are significant, which passes the Hausman test.
Table 6.
The description of the RE regression results
Results and Discussion
Regression Results Analysis
Through the mixed multiple regression and endogenous tests above, it was found that the main influencing factors of agricultural carbon emissions in China (cultivated land (CA), total power of agricultural machinery (TPAM), rural electricity consumption (REC), amount of agricultural chemical fertilizer applied (AACF), and amount of plastic film used in agriculture (APF)) had significant causal relationships with the output of agricultural economy (OAE), and the effects on the output of agricultural economy (OAE) were all positive. After increasing the control variables (such as the number of biogas projects (NBP), reservoir capacity (RC), waterlogging control area (WCA), and soil erosion control area (SECA)), the causal relationships between the main factors affecting agricultural carbon emissions in China and the total output of agricultural economy (OAE) are still significant, and the missing variables are controlled through an endogeneity test. In the process of Fixed Effect and Random Effect analysis, it is found that the influence factors of the two are slightly different. In the Fixed Effect, the Total Power of Agricultural Machinery (TPAM), Rural Electricity Consumption (REC), and the Amount of Agricultural Chemical Fertilizer Applied (AACF) had a positive impact on the Output of Agriculture Economic (OAE), while the influence of cultivated land (CA) and the amount of plastic film used in agriculture (APF) was not significant. In terms of the random effect, the total power of agricultural machinery (TPAM), rural electricity consumption (REC) and amount of plastic film used in agriculture (APF) have a positive impact on the output of agricultural economy (OAE), while cultivated land (CA) and the amount of agricultural chemical fertilizer applied (AACF) have no significant impact on the output of agricultural economy (OAE). These causal analyses indicate that the influence of agricultural carbon emissions on agricultural economic development in China is not significant in the cultivated land situation in each region, and the influence of limiting carbon emissions by controlling cultivated land is of little significance. Therefore, according to the research above, three main conclusions can be drawn: (1) Carbon emission measurement indicators such as the total power of agricultural machinery (TPAM), rural electricity consumption (REC), the amount of agricultural chemical fertilizer applied (AACF), and the amount of plastic film used in agriculture (APF) positively affect the development of China’s agricultural economy. The greater the input of these indicators is, the greater the carbon emissions, and the greater the agricultural economic growth. (2) The carbon emission index of cultivated land in all regions has no significant impact on China’s agricultural economy. It is difficult to limit carbon emissions by controlling cultivated land because the Chinese government has issued strict policies on farmland protection, and the elasticity of this index has not decreased. (3) The relationship between China’s agricultural carbon emissions and agricultural economic development is still on the left side of the inverted “U” shape of the environmental Kuznets curve, with increasing carbon emissions and increasing agricultural economic development. Since agricultural carbon emissions affect the development of China’s agricultural economy and affect China’s food security, the Chinese government should actively explore new ways to promote agricultural economic development without increasing carbon emissions.
Robustness Test Analysis
The influence of the carbon emission index on agricultural economic development also needs to be tested for robustness. Since the Output of Agriculture Economic (OAE) is always greater than or equal to 0, the dependent variable is roughly continuously distributed over positive values. Therefore, the limited dependent variable regression model (Tobit) is selected to analyze the influence of carbon emission factors on agricultural economic growth income. The basic form of the model is as follows (Formulas 9 and 10):
Table 7.
Descriptions of the Tobit regression results
In Formula 9,
In Formula 10,
Through the Tobit regression analysis of China’s agricultural economic growth by influencing factors of carbon emission, the causal relationship between the Total Power of Agricultural Machinery (TPAM), Rural Electricity Consumption (REC), the Amount of Agricultural Chemical Fertilizer applied (AACF), the Amount of Plastic Film used in agriculture (APF) and Output of Agriculture Economic (OAE) was significant. Among them, the TPAM, REC, AACF and APF have positive effects on the OAE, while the influence of Cultivated land (CA) on the OAE is not significant. Among the control variables, the Number of Biogas Projects (NBP) and the Reservoir Capacity (RC) have significant and positive effects on the OAE, while the Waterlogging Control Area (WCA), and Soil Erosion Control Area (SECA) have no significant effects on the OAE. In general, the Tobit regression basically confirmed that the influencing factors of carbon emissions had a positive impact on China’s agricultural economic growth, and the original Hypotheses H1 were valid.
Conclusions
China’s agricultural carbon emissions and agricultural economic development are the subject of heated discussion. China is a large country with a large population and large agriculture area, and the control of agricultural carbon emissions has a significant impact on the development of China’s agricultural economy. Based on the panel data of 31 provinces in China from 2019 to 2022, this paper selects a series of carbon emission correlation indicators and measurement model methods to study and discuss whether the influencing factors of China’s agricultural carbon emissions affect the development of China’s agricultural economy. Carbon emission indicators such as the total power of agricultural machinery (TPAM), rural electricity consumption (REC), and amount of agricultural chemical fertilizer applied (AACF) have a significant positive impact on the development of China’s agricultural economy. The greater the carbon emissions of these indicators are, the faster the growth of China’s agricultural economy. However, the impact of agricultural use is not significant, and it is difficult to limit carbon emissions by controlling cultivated land (CA). Moreover, to protect China’s food security, the Chinese government has introduced strict cultivated land protection policies. At present, the relationship between China’s agricultural carbon emissions and agricultural economic development is still on the left side of the inverted “U” shape of the environmental Kuznets curve when carbon emissions increase and agricultural economic development also increases. The research findings will be utilized in various aspects of agricultural production, such as the application of agricultural fertilizers, power for agricultural machinery, energy consumption, agricultural production techniques, recycling of agricultural film waste, soil quality improvement, and so forth. At present, China’s agricultural economy is still in the primary stage of agricultural development. While controlling carbon emission indicators (TPAM, REC, AACF, APF, etc.), the government should actively explore other development methods, such as agricultural biotechnology research and development, agricultural policy support and strengthening farmers’ vocational education, to continuously reduce the cost of agricultural production. Improving the scientific and technological level of agriculture and the quality level of farmers will not only increase carbon emissions but also promote agricultural economic growth and achieve high-quality agricultural development in China. This paper emphasizes the development of an agricultural economy through new technologies under the control of agricultural carbon emissions rather than encouraging agricultural development to decouple from carbon emissions because of carbon emission limits.
Policy Recommendations
The Chinese government is advised to develop a series of policies to support agricultural carbon emissions, including providing low-carbon education for farmers, offering incentives for agricultural carbon emissions reduction, and subsidizing agricultural carbon sinks. On one hand, the government should actively promote the policy of low-carbon agricultural production and provide farmers with education on the importance of low-carbon agriculture. On the other hand, the government should provide subsidies to operators who adopt low-carbon agricultural production methods, such as using organic fertilizers, agricultural machinery and equipment, agricultural film, electricity, etc. The government should also subsidize product prices and encourage banks to offer flexible credit policies for low-carbon agriculture. With these policies in place, China’s low-carbon agriculture will continue to develop and achieve high-quality development goals.
